Simple hints for …

People

History: Theory

History: Implementation

Download ANTs binaries

• Go to: http://stnava.github.io/ANTs/
• You will see links for documentation, code, binaries.
• From the folder ANTS, select a specific release, e.g.: 2.1
• Select the right version for your platform.

Download ANTsR binaries

• Go to: http://stnava.github.io/ANTsR/
• You will see links for documentation, code, binaries.
• Select the latest release for your platform then:
• R CMD INSTALL ANTsR*tar.gz

Install ANTs from source: Requires git, cmake, C++

open a terminal window and then type

$>$ git clone http://github.com/stnava/ANTs.git

$>$  mkdir bin

$>$  cd bin/

$>$  ccmake ../ANTS/

Install ANTs from source: Requires git/svn, cmake, C++

then, in cmake, type c and then g then exit back to the terminal. then:

$>$  make -j 4

and wait a while.

Overview: Program \(+\) brief description

Overview: Program \(+\) brief highlights

Overview: Program \(+\) primary reference

ANTs is good for scripting large-scale studies

App-like framework

• ANTs likes short bash scripts

• Longer scripts should use R, ANTsR or perl

• #! /usr/bin/Rscript

The Basic Toolset

• Registration: Data is in Examples/Data

ANTS 2 -m  CC[r16slice.nii.gz,r64slice.nii.gz,1,4] 
 -t SyN[0.25]  -r Gauss[3,0] -o TEST -i 50x40x30

• Segmentation

  Atropos -d 2 -a r16slice.nii.gz -x r16mask.nii.gz 
  -m [0.1,1x1]   -c [10,0]  -i kmeans[3]
  -o [Output.nii.gz,Output\_prob\_\%02d.nii.gz]

• Template building

   bash buildtemplateparallel.sh -d 3 -m 30x50x20 
  -t GR  -s CC -c 1 -o OutPrefix  *ImageName*T1x.nii.gz

ants-essential tools: a mapping, a segmentation, a template and then label-guided and multivariate versions of these.

Basic applications

• Quantify changes in cortical thickness in an individual.

• Perform a template-based study of thickness, gray matter probability, FA, rs connectivity …

• Identify multivariate relationships between modalities and predictors.

• Single-subject BOLD fmri study of resting state connectivity.

• Asymmetry study of neuroanatomy.

• In general, optimal dimensionality reduction to increase detection power.

Segmenting anatomy from an image

*See examples/segmentation\_example.sh

segmentation_example_input.tex

Multivariate anatomical segmentation

*See examples/segmentation2\_example.sh

segmentation2_example_input.tex

Meaning of segmentation parameters

What happens when i vary each parameter?

-m [0.1,1x1] -m [0.2,1x1]  -m [0.5,1x1]

vs

-i kmeans[ k ]  k = 2 , 3 , 4

Vary MRF param and K in k-means.

Cortical thickness from imaging data

See examples/thickness\_example.sh

thickness_example_input.tex

• Can vary smoothness, priors on thickness, etc. See help.

Template-based transformations

How did we compute that transformation?

see cars.sh

Mapping two images

See examples/registration\_example.sh

registration_example_input.tex

Mapping two images

Can vary smoothness, metrics, etc. See help.

Segmenting anatomy from an image Now With Priors!

See examples/segmentation3\_example.sh

via segmentation3_example_input.tex

What is the effect of N4? Should one loop over N4 and Atropos?

Meaning of ANTs registration parameters

What happens when i vary each parameter?

• Robustness increases with regularization

• -r Gauss[6,3] $>$ -r Gauss[3,0]

• Flexibility decreases with regularization

• -r Gauss[6,3] $<$ -r Gauss[3,0]

• Robustness increases with correlation window
• -m CC[ . , . , 1 , 4 ] \(<\) -m CC[ . , . , 1 , 6 ]

• but computation time also increases

• Details matter: pre-processing, feature extraction, etc.

• Successful affine step is essential!!

• Step-size increases stability but slows convergence

• SyN[ 0.1 ] more stable than SyN[ 0.25 ].

Check the affine mapping between two images

See examples/registration\_affine\_example.sh

registration_affine_example_input.tex

fixed versus affinely registered image versus original moving

Coordinates of computation time

• 2D 256\(^2\) pixels intensity difference (MSQ) registration \(\approx\)

• 3D 256\(^3\) voxels correlation-8 ( CC[ . , . , 1, 8] ) could take if you use full-resolution and the images are very different.

• or it could take if you use low-resolution and the images are very similar.

Multiple metrics driving registration

face example and why we use LMs @ the same time as data

See examples/registration\_mm\_example.sh

registration_mm_example_input.tex

fixed versus …

Landmark-based registration 1

Landmark-based registration 2

See examples/registration\_lm\_example.sh

registration_lm_example_input.tex

Landmark-based registration 3

fixed versus …

Other Landmark-based registration tools

• for 3D
• ANTSUseLandmarkImagesToGetAffineTransform lm1.nii.gz lm2.nii.gz affine outaffine.txt
• ANTSUseLandmarkImagesToBSplineDisplacementField lm1.nii.gz lm2.nii.gz outLMWarp.nii.gz 10x10x10 6 3 0
• Then use WarpImageMultiTransform / antsApplyTransform to apply the warp to the relevant image.

Template construction

See examples/btp\_example.sh

btp_example_input.tex

all ``subjects’’

Template construction output

subjects after iteration 1

Template construction output

subjects after iteration 2

Multi-template labeling

btp2_example_input.tex

The template and its groupwise segmentation.

Identifying local predictors of global differences

\[ \text{volume}_{\text{local}} \approx 1 + \text{volume}_{\text{global}} \]

ANTs multivariate voxel-wise statistics

btp3_example_input.tex

The p-value is reported to the screen

…

Now we can compare to standard voxel-wise statistics using R …

…. voxel-wise statistics in R

predictor<-read.csv("/Users/stnava/Documents/writing/ANTS_MultiModality/examples/data/phantpredictors.csv")
predictor<-read.csv("/Users/stnava/Documents/writing/ANTS_MultiModality/examples/templateex/globalvols.csv")
logjac<-read.csv("/Users/stnava/Documents/writing/ANTS_MultiModality/examples/templateex/phantomGlogjacs.csv")
attach( logjac ) ; attach( predictor )
nvox<-ncol(logjac)
pvals<-rep(NA,nvox)
for ( x in c(1:nvox) ) 
{ 
  voxels<-logjac[,x]
  lmres<-summary(lm( voxels ~  vol ))
  coeff<-coefficients( lmres )
  pval<-coeff[2,4]
  pvals[x]<-pval
}
qvals<-p.adjust(pvals)
print(min(qvals))

## [1] 0.008211303

Comparison of results

SCCAN p-value \(\approx 0.003\) minimum FDR-corrected p-value \(\approx 0.009\) but results are similar. FDR threshold \(=0.05\).

Templates for all modalities

Templates for the same population T1, FA and BOLD.

We exploit population templates to extract the brain and set-up modality specific brain extraction and segmentation.

Longitudinal analysis with ANTs

Look at your data to verify the quality of the rigid mapping!

Processing of diffusion tensor data.

• Intra-subject matching ( T1 \(\rightarrow\) FA )
• Compose warps for template space warp
• Warp and reorient with preservation of principal direction
• Get derived images (FA, MD, RGB, etc)

Processing of diffusion tensor data.

Subject FA & Subject T1 & Aligned composite

Time series / rsfMRI analysis with ANTs

See examples/motioncorr\_example.sh motioncorr_example_input.tex

• mi — mutual information metric

• Rigid — use rigid map to the average with gradient step 0.05

• other params — smoothing, scale estimation, iterations …

Time series / rsfMRI analysis with ANTs

Time series / rsfMRI analysis with ANTs

Time series / rsfMRI analysis with ANTs}

Extracting a resting state network

See examples/rsfnodes\_example.sh

rsfnodes_example_input.tex

Then build a graph ( see iGraph ).

How does structure and functional connectivity elaborate during development?

open data

Reproducibility

• ANTs is ideal for producing reproducible research.

• It is multi-platform and portable.

• It can easily be modified for \(+++\) different types of studies.

Reproducibility

• ANTs allows one to access the powers of both ITK and R.

• We aim to support user needs so if you can’t find something, just ask …. Questions?

Acknowledgements

• NIBIB for software development support under grant R01-EB006266-01.
• The ITK development team.
• Dr. Murray Grossman for pushing application development.
• The NLM for providing stimulus funding in support of ITKv4.